---
base_model: indobenchmark/indobert-base-p2
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:133472
- loss:SoftmaxLoss
widget:
- source_sentence: Dua tim anak-anak, yang satu berwarna hijau dan yang lainnya berwarna
merah, bermain bersama dalam permainan Rugby saat hujan.
sentences:
- Tiga orang berada di dalam perahu.
- seorang pria di atas sepeda
- Tim rugby anak-anak, merah versus hijau bermain di tengah hujan.
- source_sentence: Seorang pria melakukan perawatan di rel kereta api
sentences:
- Dua orang terlibat dalam percakapan.
- Ada seorang wanita melakukan pekerjaan di rel kereta api.
- orang-orang duduk di bar
- source_sentence: Sepasang suami istri dengan pakaian renang berjalan di pantai.
sentences:
- pasangan itu duduk di dalam
- Pria itu sedang makan.
- Dua orang sedang berpose untuk difoto.
- source_sentence: Dua orang sedang duduk di samping api unggun bertumpuk kayu di
malam hari.
sentences:
- Seseorang memegang jeruk dan berjalan
- Orang-orang duduk di luar di malam hari.
- Orang-orang berada di luar.
- source_sentence: Wanita profesional di meja pendaftaran acara sementara pria berjas
melihat.
sentences:
- Orang-orang berkumpul untuk sebuah acara.
- Seorang wanita sedang berjalan menuju taman.
- Ada seorang anak yang tersenyum untuk difoto.
model-index:
- name: SentenceTransformer based on indobenchmark/indobert-base-p2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.23146247451934734
name: Pearson Cosine
- type: spearman_cosine
value: 0.23182555096720683
name: Spearman Cosine
- type: pearson_manhattan
value: 0.19847600869622337
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.2038189662328075
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.198744291061789
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.20385658228775938
name: Spearman Euclidean
- type: pearson_dot
value: 0.2561502821889763
name: Pearson Dot
- type: spearman_dot
value: 0.25101474046220823
name: Spearman Dot
- type: pearson_max
value: 0.2561502821889763
name: Pearson Max
- type: spearman_max
value: 0.25101474046220823
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.5914831439397401
name: Pearson Cosine
- type: spearman_cosine
value: 0.5978838704506128
name: Spearman Cosine
- type: pearson_manhattan
value: 0.5131648451956073
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.5147175261736068
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.5942850778734059
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6001963453484881
name: Spearman Euclidean
- type: pearson_dot
value: 0.5880400881430983
name: Pearson Dot
- type: spearman_dot
value: 0.5933998114680769
name: Spearman Dot
- type: pearson_max
value: 0.5942850778734059
name: Pearson Max
- type: spearman_max
value: 0.6001963453484881
name: Spearman Max
---
# SentenceTransformer based on indobenchmark/indobert-base-p2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) <!-- at revision 94b4e0a82081fa57f227fcc2024d1ea89b57ac1f -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("cassador/indobert-snli-v1")
# Run inference
sentences = [
'Wanita profesional di meja pendaftaran acara sementara pria berjas melihat.',
'Orang-orang berkumpul untuk sebuah acara.',
'Ada seorang anak yang tersenyum untuk difoto.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.2315 |
| **spearman_cosine** | **0.2318** |
| pearson_manhattan | 0.1985 |
| spearman_manhattan | 0.2038 |
| pearson_euclidean | 0.1987 |
| spearman_euclidean | 0.2039 |
| pearson_dot | 0.2562 |
| spearman_dot | 0.251 |
| pearson_max | 0.2562 |
| spearman_max | 0.251 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.5915 |
| **spearman_cosine** | **0.5979** |
| pearson_manhattan | 0.5132 |
| spearman_manhattan | 0.5147 |
| pearson_euclidean | 0.5943 |
| spearman_euclidean | 0.6002 |
| pearson_dot | 0.588 |
| spearman_dot | 0.5934 |
| pearson_max | 0.5943 |
| spearman_max | 0.6002 |
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### Recommendations
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 133,472 training samples
* Columns: <code>label</code>, <code>kalimat1</code>, and <code>kalimat2</code>
* Approximate statistics based on the first 1000 samples:
| | label | kalimat1 | kalimat2 |
|:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | int | string | string |
| details | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.47 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.62 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
| label | kalimat1 | kalimat2 |
|:---------------|:------------------------------------------------------------------|:----------------------------------------------------------------|
| <code>0</code> | <code>Seseorang di atas kuda melompati pesawat yang rusak.</code> | <code>Seseorang sedang makan malam, memesan telur dadar.</code> |
| <code>1</code> | <code>Seseorang di atas kuda melompati pesawat yang rusak.</code> | <code>Seseorang berada di luar ruangan, di atas kuda.</code> |
| <code>1</code> | <code>Anak-anak tersenyum dan melambai ke kamera</code> | <code>Ada anak-anak yang hadir</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
### Evaluation Dataset
#### Unnamed Dataset
* Size: 6,607 evaluation samples
* Columns: <code>label</code>, <code>kalimat1</code>, and <code>kalimat2</code>
* Approximate statistics based on the first 1000 samples:
| | label | kalimat1 | kalimat2 |
|:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | int | string | string |
| details | <ul><li>0: ~50.10%</li><li>1: ~49.90%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.87 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.45 tokens</li><li>max: 27 tokens</li></ul> |
* Samples:
| label | kalimat1 | kalimat2 |
|:---------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------|
| <code>1</code> | <code>Dua wanita berpelukan sambil memegang paket untuk pergi.</code> | <code>Dua wanita memegang paket.</code> |
| <code>0</code> | <code>Dua wanita berpelukan sambil memegang paket untuk pergi.</code> | <code>Orang-orang berkelahi di luar toko makanan.</code> |
| <code>1</code> | <code>Dua anak kecil berbaju biru, satu dengan nomor 9 dan satu dengan nomor 2 berdiri di tangga kayu di kamar mandi dan mencuci tangan di wastafel.</code> | <code>Dua anak dengan kaus bernomor mencuci tangan mereka.</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:-----:|:----:|:-----------------------:|:------------------------:|
| 0 | 0 | 0.2318 | - |
| 2.0 | 8342 | - | 0.5979 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.31.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers and SoftmaxLoss
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
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